lets_plot.geom_contour

lets_plot.geom_contour(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, bins=None, binwidth=None, **other_args)

Display contours of a 3d surface in 2d.

Parameters

• mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

• data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

• stat (str, default=’contour’) – The statistical transformation to use on the data for this layer, as a string.

• position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

• show_legend (bool, default=True) – False - do not show legend for this layer.

• sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.

• tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

• bins (int) – Number of levels.

• binwidth (float) – Distance between levels.

• other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns

Geom object specification.

Return type

LayerSpec

Note

geom_contour() displays contours of a 3d surface in 2d.

Computed variables:

• level : height of a contour.

geom_contour() understands the following aesthetics mappings:

• x : x-axis coordinates of the center of rectangles, forming a tessellation.

• y : y-axis coordinates of the center of rectangles, forming a tessellation.

• alpha : transparency level of a layer. Understands numbers between 0 and 1.

• color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

• size : lines width.

• linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

Examples

>>> import numpy as np
>>> from scipy.stats import multivariate_normal
>>> from lets_plot import *
>>> LetsPlot.setup_html()
>>> np.random.seed(42)
>>> n = 25
>>> x = np.linspace(-1, 1, n)
>>> y = np.linspace(-1, 1, n)
>>> X, Y = np.meshgrid(x, y)
>>> mean = np.zeros(2)
>>> cov = [[1, .5],
>>>        [.5, 1]]
>>> rv = multivariate_normal(mean, cov)
>>> Z = rv.pdf(np.dstack((X, Y)))
>>> data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
>>> ggplot(data, aes(x='x', y='y', z='z')) + geom_contour()

>>> import numpy as np
>>> from scipy.stats import multivariate_normal
>>> from lets_plot import *
>>> LetsPlot.setup_html()
>>> n = 100
>>> a, b = -1, 0
>>> x = np.linspace(-3, 3, n)
>>> y = np.linspace(-3, 3, n)
>>> X, Y = np.meshgrid(x, y)
>>> Z = np.exp(-5 * np.abs(Y ** 2 - X ** 3 - a * X - b))
>>> data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
>>> ggplot(data, aes(x='x', y='y', z='z')) + \
>>>     geom_contour(aes(color='..level..'), bins=3, size=1) + \
>>>     scale_color_gradient(low='#dadaeb', high='#3f007d')